COVID 19 GEOSPATIAL ANALYSIS¶

By Koome Derrick¶

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly
import plotly.graph_objs as go
from plotly import tools
from plotly.offline import init_notebook_mode, plot, iplot
In [2]:
df=pd.read_csv('https://raw.githubusercontent.com/datasets/covid-19/master/data/countries-aggregated.csv')
df.head()
Out[2]:
Date Country Confirmed Recovered Deaths
0 2020-01-22 Afghanistan 0 0 0
1 2020-01-23 Afghanistan 0 0 0
2 2020-01-24 Afghanistan 0 0 0
3 2020-01-25 Afghanistan 0 0 0
4 2020-01-26 Afghanistan 0 0 0
In [3]:
df['Country'].nunique()
Out[3]:
198

The dataset contains data from a good 198 countries¶

In [4]:
df['Country'].unique()
Out[4]:
array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',
       'Antarctica', 'Antigua and Barbuda', 'Argentina', 'Armenia',
       'Australia', 'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain',
       'Bangladesh', 'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin',
       'Bhutan', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana',
       'Brazil', 'Brunei', 'Bulgaria', 'Burkina Faso', 'Burma', 'Burundi',
       'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada',
       'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',
       'Comoros', 'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Costa Rica',
       "Cote d'Ivoire", 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark',
       'Diamond Princess', 'Djibouti', 'Dominica', 'Dominican Republic',
       'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea',
       'Estonia', 'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France',
       'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece',
       'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana',
       'Haiti', 'Holy See', 'Honduras', 'Hungary', 'Iceland', 'India',
       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',
       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',
       'Korea, South', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia',
       'Lebanon', 'Lesotho', 'Liberia', 'Libya', 'Liechtenstein',
       'Lithuania', 'Luxembourg', 'MS Zaandam', 'Madagascar', 'Malawi',
       'Malaysia', 'Maldives', 'Mali', 'Malta', 'Marshall Islands',
       'Mauritania', 'Mauritius', 'Mexico', 'Micronesia', 'Moldova',
       'Monaco', 'Mongolia', 'Montenegro', 'Morocco', 'Mozambique',
       'Namibia', 'Nepal', 'Netherlands', 'New Zealand', 'Nicaragua',
       'Niger', 'Nigeria', 'North Macedonia', 'Norway', 'Oman',
       'Pakistan', 'Palau', 'Panama', 'Papua New Guinea', 'Paraguay',
       'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania',
       'Russia', 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia',
       'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',
       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',
       'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',
       'Solomon Islands', 'Somalia', 'South Africa', 'South Sudan',
       'Spain', 'Sri Lanka', 'Sudan', 'Summer Olympics 2020', 'Suriname',
       'Sweden', 'Switzerland', 'Syria', 'Taiwan*', 'Tajikistan',
       'Tanzania', 'Thailand', 'Timor-Leste', 'Togo', 'Tonga',
       'Trinidad and Tobago', 'Tunisia', 'Turkey', 'US', 'Uganda',
       'Ukraine', 'United Arab Emirates', 'United Kingdom', 'Uruguay',
       'Uzbekistan', 'Vanuatu', 'Venezuela', 'Vietnam',
       'West Bank and Gaza', 'Winter Olympics 2022', 'Yemen', 'Zambia',
       'Zimbabwe'], dtype=object)
In [5]:
df.shape
Out[5]:
(161568, 5)
In [6]:
df.tail(10)
Out[6]:
Date Country Confirmed Recovered Deaths
161558 2022-04-07 Zimbabwe 246870 0 5455
161559 2022-04-08 Zimbabwe 246925 0 5457
161560 2022-04-09 Zimbabwe 246925 0 5457
161561 2022-04-10 Zimbabwe 246958 0 5457
161562 2022-04-11 Zimbabwe 247010 0 5460
161563 2022-04-12 Zimbabwe 247094 0 5460
161564 2022-04-13 Zimbabwe 247160 0 5460
161565 2022-04-14 Zimbabwe 247208 0 5462
161566 2022-04-15 Zimbabwe 247237 0 5462
161567 2022-04-16 Zimbabwe 247237 0 5462

With this data a choropleth map can be derived¶

In [7]:
fig=px.choropleth(df,locations='Country',locationmode='country names',color='Confirmed',animation_frame='Date')
In [8]:
fig.update_layout(title='Animated Choropleth Map of Covid 19 confirmed cases')
fig.show()

There is need to minimize the scope to say continent level for a better perspective¶

In [9]:
fig=px.choropleth(df,locations='Country',locationmode='country names',color='Confirmed',animation_frame='Date',scope='africa')
fig.update_layout(title='Animated Choropleth Map of Covid 19 confirmed cases for Africa')
fig.show()

Now to view the data in a Geographical Scatter Plot¶

In [10]:
fig2=px.scatter_geo(df,locations='Country',locationmode='country names',color='Confirmed',size='Confirmed',hover_name='Country',animation_frame='Date',title='Scatter Plot showing spread of Covid-19',template='plotly_dark')
fig2.show()

Now interested in visualizing the recovered cases in both choropleth and scatter maps¶

In [11]:
df.columns
Out[11]:
Index(['Date', 'Country', 'Confirmed', 'Recovered', 'Deaths'], dtype='object')
In [12]:
fig2=px.scatter_geo(df,locations='Country',locationmode='country names',color='Recovered',size='Recovered',hover_name='Country',animation_frame='Date',title='Scatter Plot showing Recovered Cases',template='plotly_dark')
fig2.show()
In [13]:
fig=px.choropleth(df,locations='Country',locationmode='country names',color='Recovered',animation_frame='Date',template='plotly_dark')
fig.update_layout(title='Animated Choropleth Map of Covid 19 recovered cases')
fig.show()
In [14]:
pip install geopy

Requirement already satisfied: geopy in c:\users\koome\anaconda3\lib\site-packages (2.4.1)
Requirement already satisfied: geographiclib<3,>=1.52 in c:\users\koome\anaconda3\lib\site-packages (from geopy) (2.0)
Note: you may need to restart the kernel to use updated packages.
In [15]:
import geopy
from geopy.geocoders import Nominatim #Nominatim is a tool used to search openStreet data by just passing some name and address
In [16]:
geolocator=Nominatim(user_agent='app')
In [17]:
location=geolocator.geocode('KICC')
location.latitude
Out[17]:
27.6812858
In [18]:
location.longitude
Out[18]:
85.3062175

KICC stands for Kenyatta International Conference Center, the iconic building in Nairobi.¶

In [19]:
df2=df.copy()
df2
Out[19]:
Date Country Confirmed Recovered Deaths
0 2020-01-22 Afghanistan 0 0 0
1 2020-01-23 Afghanistan 0 0 0
2 2020-01-24 Afghanistan 0 0 0
3 2020-01-25 Afghanistan 0 0 0
4 2020-01-26 Afghanistan 0 0 0
... ... ... ... ... ...
161563 2022-04-12 Zimbabwe 247094 0 5460
161564 2022-04-13 Zimbabwe 247160 0 5460
161565 2022-04-14 Zimbabwe 247208 0 5462
161566 2022-04-15 Zimbabwe 247237 0 5462
161567 2022-04-16 Zimbabwe 247237 0 5462

161568 rows × 5 columns

I am interested in knowing the total confirmed, revovered and deaths for each country¶

In [20]:
df2=df2.groupby(['Country'])[['Confirmed','Recovered','Deaths']].max().reset_index()
df2
Out[20]:
Country Confirmed Recovered Deaths
0 Afghanistan 178387 82586 7676
1 Albania 274462 130314 3496
2 Algeria 265739 118409 6874
3 Andorra 40709 14380 155
4 Angola 99194 39582 1900
... ... ... ... ...
193 West Bank and Gaza 656617 312320 5656
194 Winter Olympics 2022 535 0 0
195 Yemen 11817 4251 2148
196 Zambia 318467 189658 3973
197 Zimbabwe 247237 82994 5462

198 rows × 4 columns

In [21]:
df2.head(20)
Out[21]:
Country Confirmed Recovered Deaths
0 Afghanistan 178387 82586 7676
1 Albania 274462 130314 3496
2 Algeria 265739 118409 6874
3 Andorra 40709 14380 155
4 Angola 99194 39582 1900
5 Antarctica 11 0 0
6 Antigua and Barbuda 7535 1239 135
7 Argentina 9060495 4615834 128344
8 Armenia 422747 220438 8621
9 Australia 5384615 24203 6779
10 Austria 4045809 644388 16407
11 Azerbaijan 792349 333694 9705
12 Bahamas 33391 12702 789
13 Bahrain 562759 267220 1473
14 Bangladesh 1952275 1141157 29124
15 Barbados 64348 4251 383
16 Belarus 974046 443417 6899
17 Belgium 3972963 31130 31165
18 Belize 57331 13543 672
19 Benin 26952 8136 163

In order to plot the above data using a heatmap or marker cluster, I need the latitude and longitudes for the various countries¶

In [23]:
lat=[]
long=[]
geolocator=Nominatim(user_agent='app')
for country in df2['Country']:    
    location=geolocator.geocode(country)
    if country is None:
        lat.append(np.nan)
    else:
        lat.append(location.latitude)
        long.append(location.longitude)
In [24]:
df2['latitude']=lat
df2['longitude']=long
df2
Out[24]:
Country Confirmed Recovered Deaths latitude longitude
0 Afghanistan 178387 82586 7676 33.768006 66.238514
1 Albania 274462 130314 3496 1.231526 -75.892043
2 Algeria 265739 118409 6874 28.000027 2.999983
3 Andorra 40709 14380 155 42.540717 1.573203
4 Angola 99194 39582 1900 -11.877577 17.569124
... ... ... ... ... ... ...
193 West Bank and Gaza 656617 312320 5656 31.904966 35.202341
194 Winter Olympics 2022 535 0 0 45.746936 126.696493
195 Yemen 11817 4251 2148 16.347124 47.891527
196 Zambia 318467 189658 3973 -14.518912 27.558988
197 Zimbabwe 247237 82994 5462 -18.455496 29.746841

198 rows × 6 columns

Now that the data is ready, I need a basemap to perform the analysis. For that I'll use Folium.¶

In [25]:
import folium
In [26]:
basemap=folium.Map(location=[27,30],zoom_start=3)
basemap
Out[26]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Now going to map the confirmed cases on this basemap using a marker.¶

In [27]:
for id,row in df2.iterrows():
    folium.Marker(location=[row['latitude'],row['longitude']],popup=row['Confirmed']).add_to(basemap)
                            
basemap
Out[27]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Now going to do the same for Recovered and Death cases¶

In [28]:
for id,row in df2.iterrows():
    folium.Marker(location=[row['latitude'],row['longitude']],popup=row['Recovered']).add_to(basemap)
                            
basemap
Out[28]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [29]:
for id,row in df2.iterrows():
    folium.Marker(location=[row['latitude'],row['longitude']],popup=row['Deaths']).add_to(basemap)
                            
basemap
Out[29]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Now interested in doing a marker cluster for the total number of deaths for each country.¶

In [30]:
basemap2=folium.Map(zoom_start=3)

from folium.plugins import MarkerCluster
MC=MarkerCluster()

for id,row in df2.iterrows():
    MC.add_child(folium.Marker(location=[row['latitude'],row['longitude']],popup=row['Confirmed']))
basemap2.add_child(MC)  
                 
                 
basemap2
Out[30]:
Make this Notebook Trusted to load map: File -> Trust Notebook

Now let's plot a geographical heat map¶

In [32]:
basemap2=folium.Map(zoom_start=3)

from folium.plugins import HeatMap
In [33]:
(HeatMap(data=df2[['latitude','longitude','Deaths']],radius=20)).add_to(basemap2)
basemap2
Out[33]:
Make this Notebook Trusted to load map: File -> Trust Notebook
In [ ]: